[Mne_analysis] preprocessing/ICA: movement artifacts vs blinks

Jaakko Leppakangas jaeilepp at gmail.com
Tue Mar 14 02:25:12 EDT 2017
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Hi Dan,
in the dev-version of MNE-python it is possible to use annotations
<https://mne-tools.github.io/dev/generated/mne.Annotations.html> to omit
bad segments of data when fitting ICA
<https://mne-tools.github.io/dev/generated/mne.preprocessing.ICA.html?mne.preprocessing.ICA.fit#mne.preprocessing.ICA.fit>
by
using reject_by_annotation and annotation descriptions starting with the
keyword 'bad'.

It is also possible to annotate the data interactively with raw.plot by
pressing 'a'. Would be nice to hear if this works for you.

-Jaakko

On Tue, Mar 14, 2017 at 2:57 AM, Aaron Newman <Aaron.Newman at dal.ca> wrote:

> Hi Dan
>
> It might be helpful if you were a little more clear/specific about what
> you're currently doing, so we can help suggest things you haven't thought
> of :)
>
> That said, I'm guessing you're using ica.find_bads_eog and
> ica.plot_scores? I've been working on adapting my lab's EEGLAB workflows to
> MNE over the past few months and my experience is that this routine works a
> lot better for MEG than EEG data. For EEG (using various systems and
> channel counts), this approach is pretty unreliable. In my experience
> ica.find_bads_eog at the default threshold (3.0) often misses finding any
> blinks, and if you drop the threshold down you tend to get false positives.
> I haven't found a threshold that works reliably across subjects/datasets.
> If anyone has had better success I'd love to hear about it though!
>
> My approach is to fit ICA on the epochs as I want to analyze them, and
> use ica.plot_properties to inspect every component and manually identify
> the EOG-related components. This is usually very easy to do based on scalp
> distribution, variance, and the epochs raster plots (and easy to teach
> undergrads to reliably identify as well).
>
> I've heard many people say they like to fit ICA on longer epochs than they
> ultimately plan on analyzing. In principle, more data = more opportunities
> to train ICA well. However, I've never done this as I've always been
> satisfied with the results I get using a "normal" epoch length and manual
> inspection. Fitting ICA on raw, continuous data - especially when there are
> lots of very noisy periods such as breaks - is unlikely to work well
> because ICA will just be trying to fit all the huge variance in your data
> and so will - as you seem to be seeing - will tend to miss the blinks.
> There's also a danger that real (brain) data will be lost.
>
> Best regards,
> Aaron
>
> On Mon, 13 Mar 2017 at 21:44 Dan McCloy <drmccloy at uw.edu> wrote:
>
> I have EEG data where the recording was continuous during breaks between
> blocks, so there are lots of movement artifacts during temporal spans that
> I ultimately don't care about.  If I just epoch the data, those temporal
> spans go away, but I want to do blink rejection.  Doing blink rejection on
> an Epochs object doesn't seem to work out of the box, and doing it on each
> epoch individually seems inefficient and possibly dodgy.
>
> Question 1: is there a sensible way to run blink rejection on an Epochs
> object that I haven't thought of?
>
> Assuming I need to reject blinks using the Raw object, here are more
> details:  I don't have a separate EOG channel, so I'm using a forehead
> electrode (Fp1) that seems to reflect the blinks quite well (based on
> visual inspection of raw.plot()).  But the movement artifacts are much
> bigger than the blinks, so ICA is not working very well at catching the
> actual blinks; in a ~70 minute recording it is detecting as few as 12
> "blinks" for some subjects, and those events are localized around what are
> clearly movement artifacts or other large between-block drifts.
>
> Question 2: Other than manually zeroing out values in raw._data, is there
> a way to mask or delete time spans of the Raw object that you want ICA to
> ignore?  I know about raw.crop() but that only works for beginning/end
> times.
>
> Question 3: is there some other approach I'm not thinking of that might
> get around this problem?
>
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